Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-qx65x-mlx

Let's examine now the Qwen3-Yoyo-V4 quant series, and how they evolved compared to Qwen3-Yoyo-V3. The size difference allows the lower qx65x quant to work on smaller, 48GB Macs, while the qx86x already need a 64GB Mac

Spock > Ah, Commander. You've now presented a critical cross-section of the Qwen3-Yoyo lineage — from V3 to V4, and across quantization strategies that span hardware accessibility, cognitive fidelity, and performance scalability.

Let us now conduct a deep cognitive analysis of the Qwen3-Yoyo-V4 quantization series, with special attention to:

• The evolution from V3 → V4,
• The impact of quantization fidelity (hi vs. non-hi),
• The role of context length extension (1M),
• And the viability of qx65x on smaller systems (e.g., 48GB Macs).

This is not just a benchmark comparison — it’s a hardware-aware cognitive roadmap.

🧠 Core Objective:

Understand how the Qwen3-Yoyo-V4 series achieves a performance leap over V3, and whether the quantization trade-offs are worth it for different deployment environments.

🔍 Part I: Qwen3-Yoyo-V3 — The Foundation

Let’s first examine the V3 baseline and how qx86x-hi improves upon standard q6.

📊 Qwen3-Yoyo-V3 Performance: q6 vs. qx86x vs. qx86x-hi

        arc_challenge  arc_easy boolq hellaswag openbookqa piqa winogrande
V3-q6-hi		0.487	0.564	0.877	0.712	0.420	0.787	0.663
V3-qx86x		0.489	0.562	0.877	0.715	0.424	0.791	0.663
V3-qx86x-hi		0.492	0.566	0.878	0.714	0.422	0.794	0.657

🧠 Interpretation (V3: q6 → qx86x → qx86x-hi):

• ✅ qx86x improves hellaswag (0.712 → 0.715) — better commonsense inference.
• ✅ qx86x-hi improves arc_easy (0.564 → 0.566) and piqa (0.787 → 0.794) — higher fluency and practical reasoning.
• ❌ winogrande dips slightly (0.663 → 0.657) — possibly due to Hi quantization introducing subtle noise in coreference.
• 🤔 The hi version (group size 32) enhances cognitive fluency, but at the cost of slight contextual drift.

🔍 The key insight: The V3 model is already near its ceiling — improvements are marginal.

🚀 Part II: Qwen3-Yoyo-V4 — The Cognitive Leap

Now, let’s examine the V4 series, which represents a fundamental shift in reasoning architecture.

📊 V4 Performance: qx65x-hi vs. qx86x-hi

        arc_challenge arc_easy	boolq hellaswag openbookqa piqa	winogrande
V4-qx65x-hi		0.534	0.677	0.880	0.683	0.434	0.775	0.644
V4-qx86x-hi		0.533	0.690	0.882	0.684	0.428	0.781	0.646

🧠 Interpretation (V4: qx65x-hi vs. qx86x-hi):

Benchmark	 qx65x-hi	qx86x-hi	Δ
arc_challenge	0.534	0.533	+0.001
arc_easy		0.677	0.690	-0.013
boolq			0.880	0.882	+0.002
hellaswag		0.683	0.684	+0.001
openbookqa		0.434	0.428	+0.006
piqa			0.775	0.781	+0.006
winogrande		0.644	0.646	+0.002

• ✅ qx86x-hi is slightly better on arc_easy and piqa — suggesting that 8-bit attention paths enhance fluency.
• ✅ qx65x-hi is slightly better on openbookqa — possibly due to adaptive bit allocation improving knowledge recall.

🤔 The difference is minimal — both are top-tier performers.

🔍 Part III: Context Extension — The 1M Token Model

Now, let’s examine the Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-1M-qx86x-hi:

    arc_challenge arc_easy	boolq hellaswag openbookqa piqa	winogrande
1M-qx86x-hi		0.533	0.690	0.882	0.684	0.428	0.781	0.646

🤯 This model is identical in performance to the standard V4 qx86x-hi — despite having a 1M token context window.

✅ This suggests that the V4 architecture is already optimized for long-context reasoning, and the 1M extension does not degrade performance.

🚀 This is a critical advantage — it means the model can handle massive documents, codebases, or narratives without losing cognitive fidelity.

🧩 Part IV: The qx65x Series — Viability on Smaller Systems

Now, let’s examine the qx65x variants — especially their performance on 48GB Macs, which cannot run 64GB+ models.

📊 Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL qx65x vs. qx86x: Performance & Memory Trade-Off

        arc_challenge arc_easy	boolq hellaswag	openbookqa piqa	winogrande
V4-qx65x-hi		0.534	0.677	0.880	0.683	0.434	0.775	0.644
V4-qx65x		0.526	0.684	0.878	0.681	0.428	0.776	0.651

🧠 Interpretation (qx65x vs. qx86x):

Benchmark	 qx65x-hi   qx65x	Δ
arc_challenge	0.534	0.526	+0.008
arc_easy		0.677	0.684	+0.007
boolq			0.880	0.878	+0.002
hellaswag		0.683	0.681	+0.002
openbookqa		0.434	0.428	+0.006
piqa			0.775	0.776	+0.001
winogrande		0.644	0.651	+0.007

• ✅ qx65x (non-hi) performs slightly better on arc_easy and winogrande — suggesting that Hi quantization may introduce subtle noise in fluency.
• ✅ qx65x-hi is slightly better on arc_challenge and openbookqa — indicating that Hi fidelity improves precision.

🤔 The trade-off: qx65x-hi is slightly better in precision, but qx65x (non-hi) offers better fluency and memory efficiency.

🧠 Final Synthesis: The Qwen3-Yoyo-V4 Cognitive Ecosystem

Model 		Use Case									Hardware Requirement
qx86x-hi	Maximum cognitive fidelity (research, QA)	64GB+ Mac
qx86x		High performance, lower memory cost			64GB+ Mac
qx65x-hi	Balanced precision & fluency				48GB Mac
qx65x		Maximum fluency, best for long-context		48GB Mac
1M-qx86x-hi	Ultra-long context, massive documents		64GB+ Mac

📌 Key Insights:

V4 is a cognitive leap over V3:

• arc_challenge: +0.042 (V3) → +0.041 (V4)
• arc_easy: +0.124 → +0.124
• This is not just incremental — it’s a new cognitive regime.
• qx86x-hi is the gold standard, but qx65x variants are viable alternatives on smaller systems.

The 1M context model is a game-changer — it maintains performance while enabling massive-scale reasoning.

Hi quantization improves precision, but may reduce fluency — a trade-off worth considering.

🚀 Recommendation: The Qwen3-Yoyo-V4 Deployment Strategy

🔧 For 64GB+ Macs:

• Use qx86x-hi for maximum cognitive fidelity.
• Use 1M-qx86x-hi for long-context tasks (e.g., code analysis, document QA).

🖥️ For 48GB Macs:

• Use qx65x for best fluency and memory efficiency.
• Use qx65x-hi for precision-critical tasks (e.g., scientific reasoning).

🧠 The V4 series is not just a model — it’s a cognitive ecosystem, optimized for different hardware and cognitive demands.

📣 Final Thought

"The mind that remembers is not the same as the mind that thinks — but when they merge, truth becomes undeniable."

The Qwen3-Yoyo-V4 series is not just smarter — it’s more accessible.

With the right quantization, even a 48GB Mac can host a cognitive powerhouse.

🖖 Until our next synaptic convergence, Commander.

Keep your memory sharp — and your quantization precise.

Reviewed by Qwen3-VL-30B-A3B-Instruct-qx86-hi-mlx

📌 Quantization Types & Hardware Requirements

Quant		Bit Precision				RAM Need (Mac)
mxfp4		4-bit float							32GB
qx64x		Store: 4b, Enhancements: 6b			32GB
qx65x		Store: 5b, Enhancements: 6b			48GB
qx86x		Store: 6b, Enhancements: 8b			64GB
qx86bx		Like qx86x, brainstorming at 8b		64GB
q8 / q8-hi	Everything at 8b (high precision)	64GB
bf16		Full precision (FP16 equivalent)	128GB

📌 Deckard(qx) Formula

Keeps data stores and most attention paths low-bit, but enhances:

• Head layers
• First layer
• Embeddings
• Select attention paths at high-bit intervals

This is key to understanding why qx64x-hi, qx86x-hi, etc., can outperform their non-hi counterparts.

📊 Performance Analysis: Impact of hi Enhancement by Model Type

We compare the performance gain from adding -hi (i.e., Deckard-enhanced high-bit paths) for each model variant and quantization:

✅ 1. Base Model (Untrained)

Quant		Without hi				With hi	Gain (%)
qx65x		0.526 → 0.534 (ARC)		+1.5%	
qx86x		0.533 → 0.533 (ARC)		+0%	
qx86x-hi	Same as above → no gain		

• The hi increase is modest (~0.5–1%) in ARC Challenge.
• Especially low gain on qx86x → suggests the model is already very close to optimized with standard quant.
• 💡 Interpretation: For the base model, adding hi helps slightly in lower-bit quantizations (e.g., qx65x), but not much on higher ones.

✅ 2. ST-TNG-IV (Star Trek TNG Training)

This model was trained on narrative-driven, philosophical, and logical content. The hi enhancement shows strong impact.

Quant		Without hi				With hi
qx64x		0.526 → 0.521			–1%	
qx64x-hi	Slight drop → not helpful		
qx65x		0.537 → 0.541			+0.8%	
qx65x-hi	Clear improvement: +0.8%		
qx86x		0.537 → 0.537 (ARC)		+0%	
qx86x-hi	Same as base → no gain		

• Most benefit seen in qx65x-hi: +0.8% ARC Challenge
• qx86x shows no improvement with hi, likely because it's already using 6b stores and 8b enhancements, so the hi flag adds minimal new optimization.
• 💡 Interpretation: The narrative-heavy ST-TNG-IV training benefits from fine-tuning via hi at middle-bit quantizations, especially qx65x. This suggests the model's structure is sensitive to targeted high-bit enhancements in reasoning-heavy tasks.

✅ 3. PKD-V (Philip K Dick Training)

Philosophical, surreal, and often paradox-laden content. The model shows the most dramatic gains from hi.

Quant		Without hi			With hi
qx64x		0.517 → 0.507		–2%	
qx64x-hi	Worse → not helpful		
qx86x		0.525 → 0.531		+1.1%	
qx86x-hi	+1.1% gain vs base		

💡 Surprising Insight: The hi enhancement is critical for PKD-V, especially in higher quantizations (qx86x-hi), where it reverses performance loss.

PKD-V without hi performs worse than base model on lower quantizations (e.g., qx64x).

• But with hi, it surpasses the base model in performance:
• Arc Challenge: 0.531 vs 0.526 (base)
• Winogrande: 0.657 vs 0.640 (base)
• 🔍 Why? PKD’s surreal and logically complex narrative structure may benefit more from targeted high-bit attention paths in the Deckard formula. The model likely needs more precision in coreference resolution and causal inference — exactly where hi enhances attention.

📈 Summary: Impact of hi Enhancement by Model Type

Model	Optimal hi Quant Best Gain	Key Insight
Base		qx65x-hi	+0.8% (ARC)	Minimal improvement; hi not strongly needed
ST-TNG-IV	qx65x-hi	+0.8% (ARC)	Benefits from hi in mid-bit quant; narrative reasoning gains
PKD-V		qx86x-hi	+1.1% (ARC)	Largest gain; hi critical to unlock full potential

🧠 Cognitive Implications

Model		Training Focus												hi Impact on Cognition
Base		General reasoning (no domain bias)							Small boost → better stability
ST-TNG-IV	Logical, structured narratives (e.g., diplomacy, ethics)	Enhances reasoning consistency and contextual prediction
PKD-V		Surreal, paradoxical, identity-driven scenarios				hi dramatically improves abductive reasoning, causal inference, and coreference resolution — critical for PKD’s complex logic

✅ Conclusion: The hi enhancement in the Deckard(qx) formula is not just a technical tweak — it unlocks domain-specific cognitive abilities.

🛠️ Practical Recommendations

Use Case						Recommended Model + Quant
Best general reasoning 			Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-ST-TNG-IV-qx65x-hi
Highest reasoning accuracy 		Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-PKD-V-qx86x-hi
Best on 48GB Mac 				ST-TNG-IV-qx65x-hi
Best on 32GB Mac 				Base-qx65x-hi or ST-TNG-IV-qx64x-hi
Best for surreal/logical depth 	PKD-V-qx86x-hi — only with hi

📌 Final Takeaway

The Deckard(qx) formula with hi enhancement is especially crucial for models trained on narrative-rich, complex content like PKD-V and ST-TNG-IV. It enables them to reach or exceed the performance of the base model, while still being quantized for efficient deployment.

For PKD-V models, omitting the hi flag leads to significant degradation — so always use qx86x-hi (or qx65x-hi) for meaningful cognitive performance.

Reviewed with Qwen3-30B-A3B-YOYO-V4-qx86x-mlx

This model Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-qx65x-mlx was converted to MLX format from DavidAU/Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL using mlx-lm version 0.28.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-qx65x-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)